In [1]:
conda install -c conda-forge requests six pandas numpy osmnet pandana matplotlib geopy cartopy pyyaml scikit-learn
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /Users/radleyciego/opt/anaconda3
added / updated specs:
- cartopy
- geopy
- matplotlib
- numpy
- osmnet
- pandana
- pandas
- pyyaml
- requests
- scikit-learn
- six
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2022.5.18.1| h033912b_0 145 KB conda-forge
certifi-2022.5.18.1 | py39h6e9494a_0 150 KB conda-forge
------------------------------------------------------------
Total: 295 KB
The following packages will be UPDATED:
ca-certificates 2021.10.8-h033912b_0 --> 2022.5.18.1-h033912b_0
certifi 2021.10.8-py39h6e9494a_2 --> 2022.5.18.1-py39h6e9494a_0
Downloading and Extracting Packages
certifi-2022.5.18.1 | 150 KB | ##################################### | 100%
ca-certificates-2022 | 145 KB | ##################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Note: you may need to restart the kernel to use updated packages.
In [2]:
conda install urbanaccess -c conda-forge
Collecting package metadata (current_repodata.json): done Solving environment: done # All requested packages already installed. Note: you may need to restart the kernel to use updated packages.
In [3]:
conda install -c conda-forge pyepsg
Collecting package metadata (current_repodata.json): done Solving environment: done # All requested packages already installed. Note: you may need to restart the kernel to use updated packages.
In [4]:
import matplotlib
import pandas as pd
import cartopy.crs as ccrs
import cartopy
import matplotlib.pyplot as plt
import pandana as pdna
import osmnet as osm
from pandana.loaders import osm
import time
import pyepsg
import h5py
import urbanaccess as ua
from urbanaccess.config import settings
from urbanaccess.gtfsfeeds import feeds
from urbanaccess import gtfsfeeds
from urbanaccess.gtfs.gtfsfeeds_dataframe import gtfsfeeds_dfs
from urbanaccess.network import ua_network, load_network
from simpledbf import Dbf5
%matplotlib inline
In [5]:
settings.to_dict()
Out[5]:
{'data_folder': 'data',
'logs_folder': 'logs',
'log_file': True,
'log_console': False,
'log_name': 'urbanaccess',
'log_filename': 'urbanaccess',
'txt_encoding': 'utf-8',
'gtfs_api': {'gtfsdataexch': 'http://www.gtfs-data-exchange.com/api/agencies?format=csv'}}
In [6]:
settings.log_console = True
In [7]:
settings.log_console = False
In [8]:
feeds.to_dict()
Out[8]:
{'gtfs_feeds': {}}
In [9]:
# GTFS feeds can be added using custom URLs by adding a dictionary with the key as the name of the transit agency
feeds.add_feed(add_dict={'MTA New York City Transit Authority': 'http://web.mta.info/developers/data/nyct/subway/google_transit.zip'})
feeds.add_feed(add_dict={'MTA Bronx Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_bronx.zip'})
feeds.add_feed(add_dict={'MTA Brooklyn Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_brooklyn.zip'})
feeds.add_feed(add_dict={'MTA Manhattan Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_manhattan.zip'})
feeds.add_feed(add_dict={'MTA Queens Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_queens.zip'})
feeds.add_feed(add_dict={'MTA Staten Island Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_staten_island.zip'})
Added 1 feeds to gtfs_feeds: {'MTA New York City Transit Authority': 'http://web.mta.info/developers/data/nyct/subway/google_transit.zip'}
Added 1 feeds to gtfs_feeds: {'MTA Bronx Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_bronx.zip'}
Added 1 feeds to gtfs_feeds: {'MTA Brooklyn Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_brooklyn.zip'}
Added 1 feeds to gtfs_feeds: {'MTA Manhattan Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_manhattan.zip'}
Added 1 feeds to gtfs_feeds: {'MTA Queens Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_queens.zip'}
Added 1 feeds to gtfs_feeds: {'MTA Staten Island Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_staten_island.zip'}
In [10]:
# List the GTFS feeds in feed object ready to download
feeds.to_dict()
Out[10]:
{'gtfs_feeds': {'MTA New York City Transit Authority': 'http://web.mta.info/developers/data/nyct/subway/google_transit.zip',
'MTA Bronx Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_bronx.zip',
'MTA Brooklyn Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_brooklyn.zip',
'MTA Manhattan Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_manhattan.zip',
'MTA Queens Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_queens.zip',
'MTA Staten Island Transit Bus': 'http://web.mta.info/developers/data/nyct/bus/google_transit_staten_island.zip'}}
In [11]:
# Download function to download all feeds in the feeds object at once.
gtfsfeeds.download()
6 GTFS feed(s) will be downloaded here: data/gtfsfeed_zips MTA New York City Transit Authority GTFS feed downloaded successfully. Took 3.58 seconds for 4,648,447.0KB MTA Bronx Transit Bus GTFS feed downloaded successfully. Took 5.26 seconds for 8,786,764.0KB MTA Brooklyn Transit Bus GTFS feed downloaded successfully. Took 11.50 seconds for 16,590,008.0KB MTA Manhattan Transit Bus GTFS feed downloaded successfully. Took 4.71 seconds for 7,613,854.0KB MTA Queens Transit Bus GTFS feed downloaded successfully. Took 4.72 seconds for 8,260,856.0KB MTA Staten Island Transit Bus GTFS feed downloaded successfully. Took 3.65 seconds for 6,040,530.0KB GTFS feed download completed. Took 33.41 seconds MTA Staten Island Transit Bus.zip successfully extracted to: data/gtfsfeed_text/MTA Staten Island Transit Bus MTA Brooklyn Transit Bus.zip successfully extracted to: data/gtfsfeed_text/MTA Brooklyn Transit Bus MTA New York City Transit Authority.zip successfully extracted to: data/gtfsfeed_text/MTA New York City Transit Authority MTA Bronx Transit Bus.zip successfully extracted to: data/gtfsfeed_text/MTA Bronx Transit Bus MTA Manhattan Transit Bus.zip successfully extracted to: data/gtfsfeed_text/MTA Manhattan Transit Bus MTA Queens Transit Bus.zip successfully extracted to: data/gtfsfeed_text/MTA Queens Transit Bus GTFS feed zipfile extraction completed. Took 1.64 seconds for 6 files
In [12]:
# After downloading the data, load GTFS feeds into a combined network of Pandas DataFrames.
# One or multiple feeds inside a root folder can be specified using the gtfsfeed_path paramater.
# Setting a bounding box limits GTFS data to specified area (NYC)
validation = True
verbose = True
# bbox for New York City
bbox = (-74.25909,40.477399,-73.700181,40.916178)
remove_stops_outsidebbox = True
append_definitions = True
loaded_feeds = ua.gtfs.load.gtfsfeed_to_df(gtfsfeed_path='/Users/radleyciego/data/gtfsfeed_text/',
validation=validation,
verbose=verbose,
bbox=bbox,
remove_stops_outsidebbox=remove_stops_outsidebbox,
append_definitions=append_definitions)
Checking GTFS text file header whitespace... Reading files using encoding: utf-8 set in configuration.
GTFS text file header whitespace check completed. Took 1.53 seconds
--------------------------------
Processing GTFS feed: MTA Bronx Transit Bus
The unique agency id: mta_new_york_city_transit was generated using the name of the agency in the agency.txt file.
Unique agency id operation complete. Took 0.05 seconds
Unique GTFS feed id operation complete. Took 0.01 seconds
No GTFS feed stops were found to be outside the bounding box coordinates
MTA Bronx Transit Bus GTFS feed stops: coordinates are in northwest hemisphere. Latitude = North (90); Longitude = West (-90).
Appended route type to stops
Appended route type to stop_times
--------------------------------
--------------------------------
Processing GTFS feed: MTA Staten Island Transit Bus
The unique agency id: mta_new_york_city_transit was generated using the name of the agency in the agency.txt file.
Unique agency id operation complete. Took 0.03 seconds
Unique GTFS feed id operation complete. Took 0.00 seconds
No GTFS feed stops were found to be outside the bounding box coordinates
MTA Staten Island Transit Bus GTFS feed stops: coordinates are in northwest hemisphere. Latitude = North (90); Longitude = West (-90).
Appended route type to stops
Appended route type to stop_times
--------------------------------
--------------------------------
Processing GTFS feed: MTA Queens Transit Bus
The unique agency id: mta_new_york_city_transit was generated using the name of the agency in the agency.txt file.
Unique agency id operation complete. Took 0.05 seconds
Unique GTFS feed id operation complete. Took 0.01 seconds
No GTFS feed stops were found to be outside the bounding box coordinates
MTA Queens Transit Bus GTFS feed stops: coordinates are in northwest hemisphere. Latitude = North (90); Longitude = West (-90).
Appended route type to stops
Appended route type to stop_times
--------------------------------
--------------------------------
Processing GTFS feed: MTA New York City Transit Authority
The unique agency id: mta_new_york_city_transit was generated using the name of the agency in the agency.txt file.
Unique agency id operation complete. Took 0.02 seconds
Unique GTFS feed id operation complete. Took 0.00 seconds
No GTFS feed stops were found to be outside the bounding box coordinates
MTA New York City Transit Authority GTFS feed stops: coordinates are in northwest hemisphere. Latitude = North (90); Longitude = West (-90).
Appended route type to stops
Appended route type to stop_times
--------------------------------
--------------------------------
Processing GTFS feed: MTA Brooklyn Transit Bus
The unique agency id: mta_new_york_city_transit was generated using the name of the agency in the agency.txt file.
Unique agency id operation complete. Took 0.08 seconds
Unique GTFS feed id operation complete. Took 0.01 seconds
No GTFS feed stops were found to be outside the bounding box coordinates
MTA Brooklyn Transit Bus GTFS feed stops: coordinates are in northwest hemisphere. Latitude = North (90); Longitude = West (-90).
Appended route type to stops
Appended route type to stop_times
--------------------------------
--------------------------------
Processing GTFS feed: MTA Manhattan Transit Bus
The unique agency id: mta_new_york_city_transit was generated using the name of the agency in the agency.txt file.
Unique agency id operation complete. Took 0.04 seconds
Unique GTFS feed id operation complete. Took 0.01 seconds
No GTFS feed stops were found to be outside the bounding box coordinates
MTA Manhattan Transit Bus GTFS feed stops: coordinates are in northwest hemisphere. Latitude = North (90); Longitude = West (-90).
Appended route type to stops
Appended route type to stop_times
--------------------------------
Added descriptive definitions to stops, routes, stop_times, and trips tables
Successfully converted ['departure_time'] to seconds past midnight and appended new columns to stop_times. Took 19.49 seconds
6 GTFS feed file(s) successfully read as dataframes:
MTA Bronx Transit Bus
MTA Staten Island Transit Bus
MTA Queens Transit Bus
MTA New York City Transit Authority
MTA Brooklyn Transit Bus
MTA Manhattan Transit Bus
Took 40.97 seconds
In [13]:
# The feeds output, now a global urbanaccess_gtfs_df object, can be accessed with the specified variable loaded_feeds.
loaded_feeds.stops.head()
Out[13]:
| stop_id | stop_name | stop_desc | stop_lat | stop_lon | zone_id | stop_url | location_type | parent_station | unique_agency_id | unique_feed_id | route_type | stop_code | location_type_desc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100014 | BEDFORD PK BL/GRAND CONCOURSE | NaN | 40.872562 | -73.888156 | NaN | NaN | 0 | NaN | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3.0 | NaN | stop |
| 1 | 100017 | PAUL AV/W 205 ST | NaN | 40.876836 | -73.889710 | NaN | NaN | 0 | NaN | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3.0 | NaN | stop |
| 2 | 100018 | PAUL AV/W MOSHOLU PY S | NaN | 40.880392 | -73.886081 | NaN | NaN | 0 | NaN | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3.0 | NaN | stop |
| 3 | 100019 | GRAND CONCOURSE/E 138 ST | NaN | 40.813496 | -73.929489 | NaN | NaN | 0 | NaN | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3.0 | NaN | stop |
| 4 | 100020 | GRAND CONCOURSE/E 144 ST | NaN | 40.816812 | -73.928001 | NaN | NaN | 0 | NaN | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3.0 | NaN | stop |
In [14]:
loaded_feeds.stops.unique_agency_id.unique()
Out[14]:
array(['mta_new_york_city_transit'], dtype=object)
In [15]:
# View the transit stop locations
loaded_feeds.stops.plot(kind='scatter', x='stop_lon', y='stop_lat', s=0.1)
Out[15]:
<AxesSubplot:xlabel='stop_lon', ylabel='stop_lat'>
In [16]:
loaded_feeds.routes.head()
Out[16]:
| route_id | agency_id | route_short_name | route_long_name | route_desc | route_type | route_color | route_text_color | unique_agency_id | unique_feed_id | route_url | route_type_desc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | B1 | MTA NYCT | B1 | Bay Ridge - Manhattan Beach | via 86th St / Ocean Pkwy | 3 | 00AEEF | FFFFFF | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN | Bus |
| 1 | B11 | MTA NYCT | B11 | Sunset Park - Midwood | via 49th & 50th St / Avenue J | 3 | 006CB7 | FFFFFF | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN | Bus |
| 2 | B12 | MTA NYCT | B12 | Lefferts Gardens - East New York | via Clarkson Av / Empire Blvd / East New York Av | 3 | 6CBE45 | FFFFFF | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN | Bus |
| 3 | B13 | MTA NYCT | B13 | Spring Creek - Wyckoff Hospital | via Crescent St / Jamaica Av / Wyckoff Av | 3 | FAA61A | FFFFFF | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN | Bus |
| 4 | B14 | MTA NYCT | B14 | Spring Creek - Crown Heights | via Sutter Av / Pitkin Av | 3 | 00AEEF | FFFFFF | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN | Bus |
In [17]:
loaded_feeds.stop_times.head()
Out[17]:
| trip_id | arrival_time | departure_time | stop_id | stop_sequence | pickup_type | drop_off_type | unique_agency_id | unique_feed_id | route_type | stop_headsign | shape_dist_traveled | pickup_type_desc | drop_off_type_desc | departure_time_sec | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | GH_B2-Weekday-BM-143500_BX41_901 | 23:55:00 | 23:55:00 | 104525 | 1 | 0 | 0 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3 | NaN | NaN | Regularly Scheduled | Regularly Scheduled | 86100 |
| 1 | GH_B2-Weekday-BM-143500_BX41_901 | 23:56:44 | 23:56:44 | 102795 | 2 | 0 | 0 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3 | NaN | NaN | Regularly Scheduled | Regularly Scheduled | 86204 |
| 2 | GH_B2-Weekday-BM-143500_BX41_901 | 23:58:58 | 23:58:58 | 102796 | 3 | 0 | 0 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3 | NaN | NaN | Regularly Scheduled | Regularly Scheduled | 86338 |
| 3 | GH_B2-Weekday-BM-143500_BX41_901 | 23:59:50 | 23:59:50 | 104560 | 4 | 0 | 0 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3 | NaN | NaN | Regularly Scheduled | Regularly Scheduled | 86390 |
| 4 | GH_B2-Weekday-BM-143500_BX41_901 | 24:00:49 | 24:00:49 | 102798 | 5 | 0 | 0 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | 3 | NaN | NaN | Regularly Scheduled | Regularly Scheduled | 86449 |
In [18]:
loaded_feeds.trips.head()
Out[18]:
| route_id | service_id | trip_id | trip_headsign | direction_id | shape_id | unique_agency_id | unique_feed_id | block_id | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | BX41 | GH_B2-Weekday-BM | GH_B2-Weekday-BM-143500_BX41_901 | THE HUB 150 ST via WEBSTER | 1 | BX410049 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN |
| 1 | BX41 | GH_B2-Weekday | GH_B2-Weekday-003900_BX41_901 | WILLAMSBRIDGE GUN HILL RD via WEBSTER | 0 | BX410047 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN |
| 2 | BX39 | GH_B2-Weekday | GH_B2-Weekday-007600_BX41_901 | WAKEFIELD 241 ST via WHITE PLS RD | 0 | BX390019 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN |
| 3 | BX39 | GH_B2-Weekday | GH_B2-Weekday-009500_BX41_901 | GUN HILL RD | 1 | BX390167 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN |
| 4 | BX41 | GH_B2-Weekday | GH_B2-Weekday-011300_BX41_901 | THE HUB 150 ST via WEBSTER | 1 | BX410049 | mta_new_york_city_transit | mta_bronx_transit_bus_1 | NaN |
In [19]:
loaded_feeds.calendar.head()
Out[19]:
| service_id | monday | tuesday | wednesday | thursday | friday | saturday | sunday | start_date | end_date | unique_agency_id | unique_feed_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | GH_B2-Sunday | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 20220327 | 20220619 | mta_new_york_city_transit | mta_bronx_transit_bus_1 |
| 1 | GH_B2-Weekday-SDon | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 20220328 | 20220624 | mta_new_york_city_transit | mta_bronx_transit_bus_1 |
| 2 | GH_B2-Weekday-SDon-BM | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 20220327 | 20220623 | mta_new_york_city_transit | mta_bronx_transit_bus_1 |
| 3 | GH_B2-Saturday | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 20220402 | 20220625 | mta_new_york_city_transit | mta_bronx_transit_bus_1 |
| 4 | KB_B2-Sunday | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 20220327 | 20220619 | mta_new_york_city_transit | mta_bronx_transit_bus_1 |
In [20]:
# Create a time weighted graph during Monday morning peak period
ua.gtfs.network.create_transit_net(gtfsfeeds_dfs=loaded_feeds,
day='monday',
timerange=['07:00:00', '10:00:00'],
calendar_dates_lookup=None)
Using calendar to extract service_ids to select trips. 56 service_ids were extracted from calendar 63,885 trip(s) 39.22 percent of 162,875 total trip records were found in calendar for GTFS feed(s): ['mta bronx transit bus', 'mta staten island transit bus', 'mta queens transit bus', 'mta new york city transit authority', 'mta brooklyn transit bus', 'mta manhattan transit bus'] NOTE: If you expected more trips to have been extracted and your GTFS feed(s) have a calendar_dates file, consider utilizing the calendar_dates_lookup parameter in order to add additional trips based on information inside of calendar_dates. This should only be done if you know the corresponding GTFS feed is using calendar_dates instead of calendar to specify service_ids. When in doubt do not use the calendar_dates_lookup parameter. 63,885 of 162,875 total trips were extracted representing calendar day: monday. Took 0.30 seconds There are no departure time records missing from trips following the specified schedule. There are no records to interpolate. Difference between stop times has been successfully calculated. Took 8.80 seconds Stop times from 07:00:00 to 10:00:00 successfully selected 455,243 records out of 2,433,652 total records (18.71 percent of total). Took 0.83 seconds Starting transformation process for 15,180 total trips... stop time table transformation to Pandana format edge table completed. Took 18.77 seconds Time conversion completed: seconds converted to minutes. 13,513 of 14,247 records selected from stops. Took 0.05 seconds stop time table transformation to Pandana format node table completed. Took 0.02 seconds route type successfully joined to transit edges. Took 13.16 seconds route id successfully joined to transit edges. Took 0.31 seconds Successfully created transit network. Took 53.11 seconds
Out[20]:
<urbanaccess.network.urbanaccess_network at 0x7fdb73b84400>
In [21]:
urbanaccess_net = ua.network.ua_network
In [22]:
urbanaccess_net.transit_edges.head()
Out[22]:
| node_id_from | node_id_to | weight | unique_agency_id | unique_trip_id | sequence | id | route_type | unique_route_id | net_type | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 104N_mta_new_york_city_transit | 103N_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1 | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1 | 1_mta_new_york_city_transit | transit |
| 1 | 103N_mta_new_york_city_transit | 101N_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 2 | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1 | 1_mta_new_york_city_transit | transit |
| 2 | 135S_mta_new_york_city_transit | 136S_mta_new_york_city_transit | 1.0 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1 | 1_mta_new_york_city_transit | transit |
| 3 | 136S_mta_new_york_city_transit | 137S_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 2 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1 | 1_mta_new_york_city_transit | transit |
| 4 | 137S_mta_new_york_city_transit | 138S_mta_new_york_city_transit | 1.0 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 3 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1 | 1_mta_new_york_city_transit | transit |
In [23]:
urbanaccess_net.transit_nodes.head()
Out[23]:
| x | y | unique_agency_id | route_type | stop_id | stop_name | parent_station | stop_code | zone_id | location_type | net_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| node_id | |||||||||||
| 100014_mta_new_york_city_transit | -73.888156 | 40.872562 | mta_new_york_city_transit | 3.0 | 100014 | BEDFORD PK BL/GRAND CONCOURSE | NaN | NaN | NaN | 0 | transit |
| 100017_mta_new_york_city_transit | -73.889710 | 40.876836 | mta_new_york_city_transit | 3.0 | 100017 | PAUL AV/W 205 ST | NaN | NaN | NaN | 0 | transit |
| 100018_mta_new_york_city_transit | -73.886081 | 40.880392 | mta_new_york_city_transit | 3.0 | 100018 | PAUL AV/W MOSHOLU PY S | NaN | NaN | NaN | 0 | transit |
| 100019_mta_new_york_city_transit | -73.929489 | 40.813496 | mta_new_york_city_transit | 3.0 | 100019 | GRAND CONCOURSE/E 138 ST | NaN | NaN | NaN | 0 | transit |
| 100021_mta_new_york_city_transit | -73.927096 | 40.818856 | mta_new_york_city_transit | 3.0 | 100021 | GRAND CONCOURSE/E 149 ST | NaN | NaN | NaN | 0 | transit |
In [24]:
urbanaccess_net.transit_nodes.plot(kind='scatter', x='x', y='y', s=0.1)
Out[24]:
<AxesSubplot:xlabel='x', ylabel='y'>
In [25]:
network = ua.network.load_network(dir='data', filename='nyc_net.h5')
network
Successfully read store: data/nyc_net.h5 with the following keys: ['/edges', '/nodes'] Successfully read store: data/nyc_net.h5 with the following keys: ['/edges', '/nodes']
Out[25]:
<urbanaccess.network.urbanaccess_network at 0x7fdb73b84400>
In [26]:
# Download OpenStreetMap pedestrian street network data using New York City bounding box
nodes, edges = ua.osm.load.ua_network_from_bbox(bbox=(-74.25909,40.477399,-73.700181,40.916178),
remove_lcn=True)
Requesting network data within bounding box from Overpass API in 1 request(s)
Posting to http://www.overpass-api.de/api/interpreter with timeout=180, "{'data': '[out:json][timeout:180];(way["highway"]["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]["foot"!~"no"]["pedestrians"!~"no"](40.47739900,-74.25909000,40.91617800,-73.70018100);>;);out;'}"
Downloaded 185,258.8KB from www.overpass-api.de in 21.66 seconds
Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 25.79 seconds
Returning OSM data with 1,086,444 nodes and 291,401 ways...
Edge node pairs completed. Took 267.47 seconds
Returning processed graph with 404,466 nodes and 1,217,672 edges...
Completed OSM data download and Pandana node and edge table creation in 306.13 seconds
checking for low connectivity nodes...
Generating contraction hierarchies with 8 threads.
Setting CH node vector of size 404466
Setting CH edge vector of size 1259704
Range graph removed 1322362 edges of 2519408
. 10% . 20% . 30% . 40% . 50% . 60% . 70% . 80% . 90% . 100%
1,149 out of 404,466 nodes (0.28 percent of total) were identified as having low connectivity and have been removed.
Completed OSM data download and graph node and edge table creation in 969.46 seconds
In [27]:
# Create travel time weighted pedestrian network. Pedestrian travel speed set at 3mph. osm_edges and osm_nodes will
# hold nodes and edges, respectively.
ua.osm.network.create_osm_net(osm_edges=edges,
osm_nodes=nodes,
travel_speed_mph=3)
Created OSM network with travel time impedance using a travel speed of 3 MPH. Took 0.03 seconds
Out[27]:
<urbanaccess.network.urbanaccess_network at 0x7fdb73b84400>
In [28]:
# Visualize OSM network nodes
urbanaccess_net.osm_nodes.head()
Out[28]:
| x | y | id | net_type | |
|---|---|---|---|---|
| id | ||||
| 30807308 | -73.962176 | 40.792024 | 30807308 | walk |
| 30807314 | -73.963576 | 40.790720 | 30807314 | walk |
| 30807351 | -73.966988 | 40.785589 | 30807351 | walk |
| 30978747 | -73.973425 | 40.774267 | 30978747 | walk |
| 30978752 | -73.974360 | 40.774776 | 30978752 | walk |
In [29]:
# Visualize OSM network edges
urbanaccess_net.osm_edges.head()
Out[29]:
| from | to | distance | name | ref | highway | service | bridge | tunnel | access | ... | lanes | maxspeed | hgv | hov | area | width | est_width | junction | weight | net_type | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 278630910 | 6933321234 | 278630910 | 6933321234 | 16.146700 | Boerum Place | NaN | primary | NaN | NaN | NaN | NaN | ... | 3 | 25 mph | destination | NaN | NaN | NaN | NaN | NaN | 0.200662 | walk |
| 6933321234 | 278630910 | 6933321234 | 278630910 | 16.146700 | Boerum Place | NaN | primary | NaN | NaN | NaN | NaN | ... | 3 | 25 mph | destination | NaN | NaN | NaN | NaN | NaN | 0.200662 | walk |
| 4209410680 | 6933321234 | 4209410680 | 75.472688 | Boerum Place | NaN | primary | NaN | NaN | NaN | NaN | ... | 3 | 25 mph | destination | NaN | NaN | NaN | NaN | NaN | 0.937933 | walk | |
| 4209410680 | 6933321234 | 4209410680 | 6933321234 | 75.472688 | Boerum Place | NaN | primary | NaN | NaN | NaN | NaN | ... | 3 | 25 mph | destination | NaN | NaN | NaN | NaN | NaN | 0.937933 | walk |
| 4205830390 | 3602678205 | 4205830390 | 3602678205 | 27.601179 | West 106th Street | NaN | secondary | NaN | NaN | NaN | NaN | ... | NaN | NaN | destination | NaN | NaN | NaN | NaN | NaN | 0.343012 | walk |
5 rows × 22 columns
In [30]:
urbanaccess_net.osm_nodes.plot(kind='scatter', x='x', y='y', s=0.1)
Out[30]:
<AxesSubplot:xlabel='x', ylabel='y'>
In [31]:
# Integrate pedestrian and transit networks
ua.network.integrate_network(urbanaccess_network=urbanaccess_net,
headways=False)
Loaded UrbanAccess network components comprised of:
Transit: 13,513 nodes and 440,063 edges;
OSM: 403,317 nodes and 1,215,368 edges
Connector edges between the OSM and transit network nodes successfully completed. Took 3.89 seconds
Edge and node tables formatted for Pandana with integer node ids: id_int, to_int, and from_int. Took 13.04 seconds
Network edge and node network integration completed successfully resulting in a total of 416,830 nodes and 1,719,560 edges:
Transit: 13,513 nodes 440,063 edges;
OSM: 403,317 nodes 1,215,368 edges; and
OSM/Transit connector: 27,026 edges.
Out[31]:
<urbanaccess.network.urbanaccess_network at 0x7fdb73b84400>
In [32]:
urbanaccess_net.net_nodes.head()
Out[32]:
| id | x | y | unique_agency_id | route_type | stop_id | stop_name | parent_station | stop_code | zone_id | location_type | net_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id_int | ||||||||||||
| 1 | 100014_mta_new_york_city_transit | -73.888156 | 40.872562 | mta_new_york_city_transit | 3.0 | 100014 | BEDFORD PK BL/GRAND CONCOURSE | NaN | NaN | NaN | 0.0 | transit |
| 2 | 100017_mta_new_york_city_transit | -73.889710 | 40.876836 | mta_new_york_city_transit | 3.0 | 100017 | PAUL AV/W 205 ST | NaN | NaN | NaN | 0.0 | transit |
| 3 | 100018_mta_new_york_city_transit | -73.886081 | 40.880392 | mta_new_york_city_transit | 3.0 | 100018 | PAUL AV/W MOSHOLU PY S | NaN | NaN | NaN | 0.0 | transit |
| 4 | 100019_mta_new_york_city_transit | -73.929489 | 40.813496 | mta_new_york_city_transit | 3.0 | 100019 | GRAND CONCOURSE/E 138 ST | NaN | NaN | NaN | 0.0 | transit |
| 5 | 100021_mta_new_york_city_transit | -73.927096 | 40.818856 | mta_new_york_city_transit | 3.0 | 100021 | GRAND CONCOURSE/E 149 ST | NaN | NaN | NaN | 0.0 | transit |
In [33]:
urbanaccess_net.net_edges.head()
Out[33]:
| from | to | weight | unique_agency_id | unique_trip_id | sequence | edge_id | route_type | unique_route_id | net_type | ... | lanes | maxspeed | hgv | hov | area | width | est_width | junction | from_int | to_int | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 104N_mta_new_york_city_transit | 103N_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1.0 | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6098 | 6096 |
| 1 | 103N_mta_new_york_city_transit | 101N_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 2.0 | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6096 | 6094 |
| 2 | 135S_mta_new_york_city_transit | 136S_mta_new_york_city_transit | 1.0 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6159 | 6161 |
| 3 | 136S_mta_new_york_city_transit | 137S_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 2.0 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6161 | 6163 |
| 4 | 137S_mta_new_york_city_transit | 138S_mta_new_york_city_transit | 1.0 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 3.0 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6163 | 6165 |
5 rows × 30 columns
In [34]:
urbanaccess_net.net_edges[urbanaccess_net.net_edges['net_type'] == 'transit'].head()
Out[34]:
| from | to | weight | unique_agency_id | unique_trip_id | sequence | edge_id | route_type | unique_route_id | net_type | ... | lanes | maxspeed | hgv | hov | area | width | est_width | junction | from_int | to_int | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 104N_mta_new_york_city_transit | 103N_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1.0 | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6098 | 6096 |
| 1 | 103N_mta_new_york_city_transit | 101N_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 2.0 | AFA21GEN-1091-Weekday-00_037150_1..N03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6096 | 6094 |
| 2 | 135S_mta_new_york_city_transit | 136S_mta_new_york_city_transit | 1.0 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6159 | 6161 |
| 3 | 136S_mta_new_york_city_transit | 137S_mta_new_york_city_transit | 1.5 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 2.0 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6161 | 6163 |
| 4 | 137S_mta_new_york_city_transit | 138S_mta_new_york_city_transit | 1.0 | mta_new_york_city_transit | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 3.0 | AFA21GEN-1091-Weekday-00_037300_1..S03R_mta_ne... | 1.0 | 1_mta_new_york_city_transit | transit | ... | nan | nan | nan | nan | nan | nan | nan | nan | 6163 | 6165 |
5 rows × 30 columns
In [35]:
# Save network to root data folder under file name nyc_net.h5
ua.network.save_network(urbanaccess_network=urbanaccess_net,
filename='nyc_net.h5',
overwrite_key = True)
Using existing data/nyc_net.h5 hdf5 store. Existing edges overwritten in data/nyc_net.h5 hdf5 store. Using existing data/nyc_net.h5 hdf5 store. Existing nodes overwritten in data/nyc_net.h5 hdf5 store.
In [36]:
# Load nyc_net HDF5 network file
urbanaccess_net = ua.network.load_network(filename='nyc_net.h5')
Successfully read store: data/nyc_net.h5 with the following keys: ['/edges', '/nodes'] Successfully read store: data/nyc_net.h5 with the following keys: ['/edges', '/nodes']
In [37]:
#Visualize pedestrian network
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges,
bbox=(-74.25909,40.477399,-73.700181,40.916178),
fig_height=30, margin=0.02,
edge_color='#999999', edge_linewidth=1, edge_alpha=1,
node_color='black', node_size=1.1, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 113.02 seconds
Out[37]:
(<Figure size 2751.37x2160 with 1 Axes>, <AxesSubplot:>)
In [38]:
# Visualize integrated transit and pedestrian network
edgecolor = ua.plot.col_colors(df=urbanaccess_net.net_edges, col='weight', cmap='gist_heat_r', num_bins=5)
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges,
bbox=(-74.25909,40.477399,-73.700181,40.916178),
fig_height=30, margin=0.02,
edge_color=edgecolor, edge_linewidth=1, edge_alpha=0.7,
node_color='black', node_size=0, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 96.15 seconds
Out[38]:
(<Figure size 2751.37x2160 with 1 Axes>, <AxesSubplot:>)
In [43]:
# Visualize the transit network including trains and buses, by network type (transit)
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges[urbanaccess_net.net_edges['net_type']=='transit'],
bbox=None,
fig_height=30, margin=0.02,
edge_color='#999999', edge_linewidth=1, edge_alpha=1,
node_color='black', node_size=0, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 25.72 seconds
Out[43]:
(<Figure size 2904.78x2160 with 1 Axes>, <AxesSubplot:>)
In [39]:
ua.gtfs.headways.headways(gtfsfeeds_df=loaded_feeds,
headway_timerange=['07:00:00','10:00:00'])
Stop times from 07:00:00 to 10:00:00 successfully selected 455,243 records out of 2,433,652 total records (18.71 percent of total). Took 1.04 seconds Starting route stop headway calculation for 19,491 route stops... Route stop headway calculation complete. Took 36.43 seconds headway calculation complete. Took 42.79 seconds
Out[39]:
<urbanaccess.gtfs.gtfsfeeds_dataframe.urbanaccess_gtfs_df at 0x7fdb73988b50>
In [40]:
loaded_feeds.headways.head()
Out[40]:
| count | mean | std | min | 25% | 50% | 75% | max | unique_stop_id | unique_route_id | node_id_route | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 911504 | 109.0 | 1.568807 | 3.356494 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0 | 100014_mta_new_york_city_transit | BX26_mta_new_york_city_transit | 100014_mta_new_york_city_transit_BX26_mta_new_... |
| 911505 | 109.0 | 1.568807 | 3.356494 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0 | 100014_mta_new_york_city_transit | BX26_mta_new_york_city_transit | 100014_mta_new_york_city_transit_BX26_mta_new_... |
| 911506 | 109.0 | 1.568807 | 3.356494 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0 | 100014_mta_new_york_city_transit | BX26_mta_new_york_city_transit | 100014_mta_new_york_city_transit_BX26_mta_new_... |
| 911507 | 109.0 | 1.568807 | 3.356494 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0 | 100014_mta_new_york_city_transit | BX26_mta_new_york_city_transit | 100014_mta_new_york_city_transit_BX26_mta_new_... |
| 911508 | 109.0 | 1.568807 | 3.356494 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0 | 100014_mta_new_york_city_transit | BX26_mta_new_york_city_transit | 100014_mta_new_york_city_transit_BX26_mta_new_... |
In [41]:
ua.network.integrate_network(urbanaccess_network=urbanaccess_net,
headways=True,
urbanaccess_gtfsfeeds_df=loaded_feeds,
headway_statistic='mean')
Loaded UrbanAccess network components comprised of:
Transit: 13,513 nodes and 440,063 edges;
OSM: 403,317 nodes and 1,215,368 edges
routes successfully joined to transit nodes. Took 0.81 seconds
Connector edges between the OSM and transit network nodes successfully completed. Took 6.67 seconds
mean route stop headway will be used for pedestrian to transit edge impedance.
Headway impedance calculation completed. Took 1.12 seconds
Edge and node tables formatted for Pandana with integer node ids: id_int, to_int, and from_int. Took 37.52 seconds
Network edge and node network integration completed successfully resulting in a total of 422,808 nodes and 3,788,597 edges:
Transit: 19,491 nodes 440,063 edges;
OSM: 403,317 nodes 1,215,368 edges; and
OSM/Transit connector: 2,133,166 edges.
Out[41]:
<urbanaccess.network.urbanaccess_network at 0x7fdb73b84400>
In [42]:
# Visualize integrated transit and pedestrian network, weighted by transit route frequency
edgecolor = ua.plot.col_colors(df=urbanaccess_net.net_edges, col='weight', cmap='gist_heat_r', num_bins=5)
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges,
bbox=bbox,
fig_height=30, margin=0.02,
edge_color=edgecolor, edge_linewidth=1, edge_alpha=0.7,
node_color='black', node_size=0, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 227.48 seconds
Out[42]:
(<Figure size 2751.37x2160 with 1 Axes>, <AxesSubplot:>)
In [89]:
# Load NY state block data
nybg = pd.read_csv("NY_Blocks.csv")
nybg
Out[89]:
| GEOID | ALAND20 | AWATER20 | x | y | |
|---|---|---|---|---|---|
| 0 | 360894925011010 | 0 | 21972 | -75.182586 | 44.284402 |
| 1 | 360894926002022 | 113014 | 0 | -75.216830 | 44.363189 |
| 2 | 360894926002034 | 50829 | 0 | -75.239639 | 44.356982 |
| 3 | 360894926002029 | 397018 | 0 | -75.224958 | 44.348747 |
| 4 | 360894926002010 | 58704 | 0 | -75.198572 | 44.347600 |
| ... | ... | ... | ... | ... | ... |
| 288814 | 360290091091003 | 36854 | 0 | -78.765500 | 42.990761 |
| 288815 | 360290080024000 | 12580 | 0 | -78.852498 | 42.985665 |
| 288816 | 360290150021039 | 655048 | 0 | -78.498732 | 42.748689 |
| 288817 | 360290154021007 | 0 | 15105 | -78.970125 | 42.714141 |
| 288818 | 360290175022047 | 582261 | 0 | -78.821359 | 42.489494 |
288819 rows × 5 columns
In [94]:
nyjobs = pd.read_csv("NYC_Jobs.csv")
nyjobs
Out[94]:
| GEOID | Jobs | |
|---|---|---|
| 0 | 360010001001008 | 19 |
| 1 | 360010001001009 | 15 |
| 2 | 360010001001010 | 10 |
| 3 | 360010001001011 | 51 |
| 4 | 360010001001012 | 38 |
| ... | ... | ... |
| 231550 | 361231505004017 | 4 |
| 231551 | 361231505004019 | 6 |
| 231552 | 361231505004020 | 1 |
| 231553 | 361231505004021 | 2 |
| 231554 | 361231505004022 | 3 |
231555 rows × 2 columns
In [97]:
df = pd.merge(nyjobs,
nybg,
on=['GEOID','GEOID'])
df.head()
Out[97]:
| GEOID | Jobs | ALAND20 | AWATER20 | x | y | |
|---|---|---|---|---|---|---|
| 0 | 360010001001008 | 19 | 7783 | 0 | -73.736149 | 42.676900 |
| 1 | 360010001001009 | 15 | 6149 | 0 | -73.737863 | 42.677447 |
| 2 | 360010001001010 | 10 | 37825 | 0 | -73.740476 | 42.677820 |
| 3 | 360010001001011 | 51 | 16416 | 0 | -73.741709 | 42.678003 |
| 4 | 360010001001012 | 38 | 12999 | 0 | -73.739613 | 42.677171 |
In [98]:
# Convert dataframe into .h5 file
df = pd.DataFrame(df)
df.to_hdf('nycblocks.h5', key='df', mode='a')
In [99]:
blocks = pd.read_hdf('nycblocks.h5')
# remove blocks that contain all water
blocks = blocks[blocks['ALAND20'] != 0]
print('Total number of blocks: {:,}'.format(len(blocks)))
blocks.head()
Total number of blocks: 148,432
Out[99]:
| GEOID | Jobs | ALAND20 | AWATER20 | x | y | |
|---|---|---|---|---|---|---|
| 0 | 360010001001008 | 19 | 7783 | 0 | -73.736149 | 42.676900 |
| 1 | 360010001001009 | 15 | 6149 | 0 | -73.737863 | 42.677447 |
| 2 | 360010001001010 | 10 | 37825 | 0 | -73.740476 | 42.677820 |
| 3 | 360010001001011 | 51 | 16416 | 0 | -73.741709 | 42.678003 |
| 4 | 360010001001012 | 38 | 12999 | 0 | -73.739613 | 42.677171 |
In [100]:
lng_max, lat_min, lng_min, lat_max = bbox
outside_bbox = blocks.loc[~(((lng_max < blocks["x"]) & (blocks["x"] < lng_min)) & ((lat_min < blocks["y"]) & (blocks["y"] < lat_max)))]
blocks = blocks.drop(outside_bbox.index)
print('Total number of subset blocks: {:,}'.format(len(blocks)))
Total number of subset blocks: 28,358
In [101]:
# Plot merged block dataframe
blocks.plot(kind='scatter', x='x', y='y', s=0.3)
Out[101]:
<AxesSubplot:xlabel='x', ylabel='y'>
In [103]:
# Initialize Pandana network using combined transit and pedestrian network
s_time = time.time()
transit_ped_net = pdna.Network(urbanaccess_net.net_nodes["x"],
urbanaccess_net.net_nodes["y"],
urbanaccess_net.net_edges["from_int"],
urbanaccess_net.net_edges["to_int"],
urbanaccess_net.net_edges[["weight"]],
twoway=True)
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Generating contraction hierarchies with 8 threads. Setting CH node vector of size 422808 Setting CH edge vector of size 3788597 Range graph removed 6304476 edges of 7577194 . 10% . 20% . 30% . 40% . 50% . 60% . 70% . 80% . 90% . 100% Took 46.89 seconds
In [104]:
# Set blocks onto network
blocks['node_id'] = transit_ped_net.get_node_ids(blocks['x'], blocks['y'])
In [105]:
transit_ped_net.set(blocks.node_id, variable = blocks.Jobs, name='Jobs')
In [106]:
s_time = time.time()
jobs_45 = transit_ped_net.aggregate(45, type='sum', decay='linear', name='Jobs')
jobs_30 = transit_ped_net.aggregate(30, type='sum', decay='linear', name='Jobs')
jobs_15 = transit_ped_net.aggregate(15, type='sum', decay='linear', name='Jobs')
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Took 1,575.08 seconds
In [107]:
print(jobs_30)
id_int
1 198208.416811
2 175368.463487
3 315693.745675
4 314908.160651
5 347635.869061
...
422804 2332.081280
422805 2492.003429
422806 2506.295022
422807 2618.433428
422808 2631.997514
Length: 422808, dtype: float64
In [108]:
s_time = time.time()
fig = plt.subplots(figsize=(20,20))
data_crs = ccrs.PlateCarree()
ax = plt.axes(projection=ccrs.epsg(2263))
ax.add_feature(cartopy.feature.GSHHSFeature(scale='full'), edgecolor='grey')
plt.scatter(transit_ped_net.nodes_df.x, transit_ped_net.nodes_df.y,
c=jobs_15, s=4, cmap='gist_heat_r', edgecolor='none', transform=data_crs)
cb = plt.colorbar()
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Took 1.11 seconds
In [109]:
s_time = time.time()
fig = plt.subplots(figsize=(20,20))
data_crs = ccrs.PlateCarree()
ax = plt.axes(projection=ccrs.epsg(2263))
ax.add_feature(cartopy.feature.GSHHSFeature(scale='full'), edgecolor='grey')
plt.scatter(transit_ped_net.nodes_df.x, transit_ped_net.nodes_df.y,
c=jobs_30, s=4, cmap='gist_heat_r', edgecolor='none', transform=data_crs)
cb = plt.colorbar()
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Took 0.89 seconds
In [110]:
s_time = time.time()
fig = plt.subplots(figsize=(20,20))
data_crs = ccrs.PlateCarree()
ax = plt.axes(projection=ccrs.epsg(2263))
ax.add_feature(cartopy.feature.GSHHSFeature(scale='full'), edgecolor='grey')
plt.scatter(transit_ped_net.nodes_df.x, transit_ped_net.nodes_df.y,
c=jobs_45, s=4, cmap='gist_heat_r', edgecolor='none', transform=data_crs)
cb = plt.colorbar()
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Took 0.72 seconds
